# Glossary Summary

This glossary provides concise definitions for key terms related to machine learning (ML), programming in Python, database and vector storage technologies, and concepts central to Retrieval-Augmented Generation (RAG) pipelines.

### Retrieval-Augmented Generation (RAG) Pipeline Terms

- **Adapter Pattern**: Enables interaction between incompatible interfaces by data transformation.
- **Metadata & Session**: Information about data and individual database connections.
- **Adapter Context & Chunking**: Context for data transformation and dividing data for processing.
- **Normalize Embeddings**: Process of scaling vectors to unit norm for consistent similarity metrics.

### Machine Learning Terms

- **Embeddings**: Numerical vectors representing data (text, images) to capture similarity.
- **Sentence Transformers**: Library for dense vector representations of text for semantic search and similarity.

### Database and Vector Storage Terms

- **PostgreSQL**: Open-source relational database for extensible SQL operations.
- **pgvector**: Extension for storing/searching high-dimensional vectors in PostgreSQL.
- **HNSW & IVFFlat**: Algorithms for efficient nearest neighbor search in vector data.
- **Upsert & Index**: Operations and structures for data insertion/update and speedy retrieval.

### Programming and Python Concepts

- **Generator**: Iterable in Python using `yield` for lazy item generation.
- **Context Manager**: Manages resources (files, database connections) with `with` statement.
